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Cosmic super string detection using dilated convolutional neural network with focal loss

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dc.contributor.author Ishrak, Mohammed Hasin
dc.date.accessioned 2018-11-07T06:22:52Z
dc.date.available 2018-11-07T06:22:52Z
dc.date.copyright 2018
dc.date.issued 2018
dc.identifier.other ID 14101180
dc.identifier.uri http://hdl.handle.net/10361/10819
dc.description This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. en_US
dc.description Cataloged from PDF version of thesis.
dc.description Includes bibliographical references (pages 32-38).
dc.description.abstract Cosmic string are objects of great importance and investigation for cosmic string has been done from last 20 years. There are a lot of models to detect cosmic string.But a very few are to detect the location of cosmic string.We propose a framework to detect the location of cosmic string. We used di- lated convolutional net with focal loss instead of cross-entropy to improved the performance of the framework on weak samples. The neural network we trained is able to detect and locate cosmic string on noiseless CMB temper- ature map down to a string tension of less then G =5 10􀀀9. We expect to use more accurate simulation to produce data set to improve the con dence of the model. en_US
dc.description.statementofresponsibility Mohammed Hasin Ishrak
dc.format.extent 38 pages
dc.language.iso en en_US
dc.publisher BRAC University en_US
dc.rights BRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subject Cosmic background radiation en_US
dc.subject Physics simulation en_US
dc.subject Cosmology en_US
dc.subject Cosmic string en_US
dc.subject Machine learning en_US
dc.subject Cosmic data science en_US
dc.subject Early universe en_US
dc.subject.lcsh Neural networks (Computer science)
dc.title Cosmic super string detection using dilated convolutional neural network with focal loss en_US
dc.type Thesis en_US
dc.contributor.department Department of Computer Science and Engineering, BRAC University
dc.description.degree B. Computer Science and Engineering


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